Staying competitive in today’s world is an exciting feat. A friend of mine pointed out (paraphrasing) that years ago, having basic computer skills like Microsoft Office didn’t guarantee you a job, but it gave you a competitive advantage. It turns out now, everyone expects you to have basic computer skills. Nowadays, having skills in data analytics, coding, and software engineering does not guarantee you a job, but it certainly gives you an advantage when applying. It may soon come a day when this is common knowledge, and I don’t want to be left behind—neither should you.
Data analytics? Statistics on steroids! A competent data analyst is well-versed in statistics (the theory), the application of statistics (statistics make no sense without applying them, to bring the abstracts to reality), and the software applications used to make statistical calculations faster.
Most software applications like Excel or Google Sheets have built-in functions that almost make statistical knowledge obsolete. For example, to do a T-test or distribution, you do not need to know all the mathematical formulas; all you need to do is call a function, and Excel will run it for you. While this is a good thing, it is also important to know what goes on behind the scenes for you to be a good data analyst (yes, we still have to learn statistics). Allow me to pretend like I know what a T-test is.
In order to become a capable data analyst, a blend of technical skills is required:
– Proficiency in relational databases (Redshift, SQL Server, MySQL, etc.) for querying and extracting data.
– Proficiency in programming languages (Python, Java, R, etc.) for gathering and cleaning data, and running statistical analysis.
– Expertise in data visualization (PowerBi, Tableau) for effective data presentation.
Data analytics is the process of examining and interpreting data to extract meaningful insights, patterns, and inclinations that can advise decision-making. The primary goal of data analytics is to gain an in-depth understanding of data, make informed predictions, and support evidence-based decision-making.
The first step in data analytics is data collection. This can be from various sources like databases, online platforms, etc. Once collected, the data is cleaned and organized to ensure its accuracy and relevance. Then, statistical and mathematical methods are applied to the data to identify patterns and relationships. Visualization tools are often used to present the findings in a clear and comprehensive manner.
There are different types of data analytics, including descriptive analytics, which focuses on summarizing historical data; predictive analytics, which uses historical data to make future predictions; and prescriptive analytics, which recommends actions based on analysis. As the saying goes, look from where you are. If I am to assess myself, I currently have intermediate-level knowledge of Excel, and I’m a novice in SQL. There is a lot to cover, and I have imposed a deadline on myself. By December 1, 2024, I will be competent in using SQL, Excel, and one programming language, preferably Python. I know it’s a small goal; perhaps as I develop my skills, the vision will broaden.
A Business as a Going Concern